Discovery Science

1. Front Matter

   PDF

2. Invited Papers

   1. On Iterative Algorithms with an Information Geometry Background

      • Imre Csiszár

      Pages 1-1

   2. Visual Analytics: Combining Automated Discovery with Interactive Visualizations

      • Daniel A. Keim, Florian Mansmann, Daniela Oelke, Hartmut Ziegler

      Pages 2-14

   3. Some Mathematics Behind Graph Property Testing

      • László Lovász

      Pages 15-15

   4. Finding Total and Partial Orders from Data for Seriation

      • Heikki Mannila

      Pages 16-25

   5. Computational Models of Neural Representations in the Human Brain

      • Tom M. Mitchell

      Pages 26-27

3. Learning

   1. Unsupervised Classifier Selection Based on Two-Sample Test

      • Timo Aho, Tapio Elomaa, Jussi Kujala

      Pages 28-39

   2. An Empirical Investigation of the Trade-Off between Consistency and Coverage in Rule Learning Heuristics

      • Frederik Janssen, Johannes Fürnkranz

      Pages 40-51

   3. Learning Model Trees from Data Streams

      • Elena Ikonomovska, Joao Gama

      Pages 52-63

   4. Empirical Asymmetric Selective Transfer in Multi-objective Decision Trees

      • Beau Piccart, Jan Struyf, Hendrik Blockeel

      Pages 64-75

   5. Ensemble-Trees: Leveraging Ensemble Power Inside Decision Trees

      • Albrecht Zimmermann

      Pages 76-87

   6. A Comparison between Neural Network Methods for Learning Aggregate Functions

      • Werner Uwents, Hendrik Blockeel

      Pages 88-99

4. Feature Selection

   1. Smoothed Prediction of the Onset of Tree Stem Radius Increase Based on Temperature Patterns

      • Mikko Korpela, Harri Mäkinen, Mika Sulkava, Pekka Nöjd, Jaakko Hollmén

      Pages 100-111

   2. Feature Selection in Taxonomies with Applications to Paleontology

      • Gemma C. Garriga, Antti Ukkonen, Heikki Mannila

      Pages 112-123

5. Associations

   1. Deduction Schemes for Association Rules

      • José L. Balcázar

      Pages 124-135

   2. Constructing Iceberg Lattices from Frequent Closures Using Generators

      • Laszlo Szathmary, Petko Valtchev, Amedeo Napoli, Robert Godin

      Pages 136-147

6. Discovery Processes

   1. Learning from Each Other

      • Christopher Dartnell, Éric Martin, Jean Sallantin

      Pages 148-159

   2. Comparative Evaluation of Two Systems for the Visual Navigation of Encyclopedia Knowledge Spaces

      • Wolfgang Kienreich, Peter Kraker

      Pages 160-171

   3. A Framework for Knowledge Discovery in a Society of Agents

      • Gauvain Bourgne, Vincent Corruble

      Pages 172-184

7. Learning and Chemistry

   1. Active Learning for High Throughput Screening

      • Kurt De Grave, Jan Ramon, Luc De Raedt

      Pages 185-196

It is our pleasure to present the proceedings of Discovery Science 2008, the 11th International Conference on Discovery Science held in Budapest, Hungary, October 13-16, 2008. It was co-located with ALT 2008, the 19th International Conference on Algorithmic Learning Theory, whose proceedings are available in the twin volume LNAI 5254. This combination of DS and ALT conferences has been successfully organized each year since 2002. It provides a forum for the researchersworking on many di?erent aspects of scienti?c discovery. Indeed, ALT/DS 2008 covered both the possibility to automate part of the scienti?c discoveryandthenecessarysupporttothehumanprocessofdiscoveryinscience. Interestingly, this co-location also provided the opportunity for an exciting joint program of tutorials and invited talks. The number of submitted papers was 58, i.e., slightly more than the previous year. The Program Committee members were involved in a rigorous selection process based on three reviews per paper. At the end, we selected 26 long papers thanks to the recommendations of the experts based on relevance, novelty, signi?cance, technical quality, and clarity. Although some short papers were submitted, none of them was selected.

• Clustering
• HCI
• classifier systems
• computational learning
• data analysis
• data mining
• digital encyclopedias
• grid computing
• knowledge
• knowledge discovery
• knowledge extraction
• knowledge processing
• knowledge visualization
• learning
• machine learning

• INSA Lyon, LIRIS CNRS UMR 5205, University of Lyon, Villeurbanne Cedex, France

  Jean-François Jean-Fran

• Department of Computer and Information Science, University of Konstanz, Konstanz, Germany

  Michael R. Berthold

• University of Bonn and Fraunhofer IAIS, Sankt Augustin, Germany

  Tamás Horváth

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